Method and system for analyzing operational parameter data for diagnostics and repairs

ABSTRACT

The present invention discloses system and method for analyzing operational parameter data from a malfunctioning locomotive or other large land-based, self-powered transport equipment. The method allows for receiving new operational parameter data comprising a plurality of anomaly definitions from the malfunctioning equipment. The method further allows for selecting a plurality of distinct anomaly definitions from the new operational parameter data. Respective generating steps allow for generating at least one distinct anomaly definition cluster from the plurality of distinct anomaly definitions and for generating a plurality of weighted repair and distinct anomaly definition cluster combinations. An identifying step allows for identifying at least one repair for the at least one distinct anomaly definition cluster using the plurality of weighted repair and distinct anomaly definition cluster combinations.

This application is continuing from U.S. application Ser. No. 09/688,105filed Oct. 13, 2000 now U.S. Pat. No. 6,636,771, which is aContinuation-In-Part of application Ser. No. 09/285,611 filed Apr. 2,1999 now U.S. Pat. No. 6,343,236. This application further claims thebenefit of U.S. Provisional Application 60/162,045 filed Oct. 28, 1999.

BACKGROUND OF THE INVENTION

The present invention relates generally to machine diagnostics, and morespecifically, to a system and method for processing historical repairdata and operational parameter data for predicting one or more repairsfrom new operational parameter data from a malfunctioning machine.

A machine such as locomotive includes elaborate controls and sensorsthat generate faults when anomalous operating conditions of thelocomotive are encountered. Typically, a field engineer will look at afault log and determine whether a repair is necessary.

Approaches like neural networks, decision trees, etc., have beenemployed to learn over input data to provide prediction, classification,and function approximation capabilities in the context of diagnostics.Often, such approaches have required structured and relatively staticand complete input data sets for learning, and have produced models thatresist real-world interpretation.

Another approach, Case Based Reasoning (CBR), is based on theobservation that experiential knowledge (memory of past experiences—orcases) is applicable to problem solving as learning rules or behaviors.CBR relies on relatively little pre-processing of raw knowledge,focusing instead on indexing, retrieval, reuse, and archival of cases.In the diagnostic context, a case generally refers to a problem/solutiondescription pair that represents a diagnosis of a problem and anappropriate repair. More particularly, a case is a collection of faultlog and corresponding operational and snapshot data patterns and otherparameters and indicators associated with one specific repair event inthe machine under consideration.

CBR assumes cases described by a fixed, known number of descriptiveattributes. Conventional CBR systems assume a corpus of fully valid or“gold standard” cases that new incoming cases can be matched against.

U.S. Pat. No. 5,463,768 discloses an approach which uses error log dataand assumes predefined cases with each case associating an input errorlog to a verified, unique diagnosis of a problem. In particular, aplurality of historical error logs are grouped into case sets of commonmalfunctions. From the group of case sets, common patterns, i.e.,consecutive rows or strings of data, are labeled as a block. Blocks areused to characterize fault contribution for new error logs that arereceived in a diagnostic unit.

For a continuous fault code stream where any or all possible fault codesmay occur from zero to any finite number of times and the fault codesmay occur in any order, predefining the structure of a case is nearlyimpossible.

U.S. Pat. No. 6,343,236, assigned to the same assignee of the presentinvention, discloses a system and method for processing historicalrepair data and fault log data, which is not restricted to sequentialoccurrences of fault log entries and which provides weighted repair anddistinct fault cluster combinations, to facilitate analysis of new faultlog data from a malfunctioning machine. Further, U.S. Pat. No.6,415,395, assigned to the same assignee of the present invention,discloses a system and method for analyzing new fault log data from amalfunctioning machine in which the system and method are not restrictedto sequential occurrences of fault log entries, and wherein the systemand method predict one or more repair actions using predeterminedweighted repair and distinct fault cluster combinations. Additionally,U.S. Pat. No. 6,336,065, assigned to the same assignee of the presentinvention, discloses a system and method that uses snapshot observationsof operational parameters from the machine in combination with the faultlog data in order to further enhance the predictive accuracy of thediagnostic algorithms used therein.

It is believed that the inventions disclosed in the foregoing patentapplications provide substantial advantages and advancements in the artof diagnostics. It would be desirable, however, to provide a system andmethod that uses anomaly definitions based on operational parameters togenerate diagnostics and repair data. The anomaly definitions aredifferent from faults in the sense that the information used can betaken in a relatively wide time window, whereas faults, or even faultdata combined with snapshot data, are based on discrete behavioroccurring at one instance in time. The anomaly definitions, however, maybe advantageously analogized to virtual faults and thus such anomalydefinitions can be learned using the same diagnostics algorithms thatcan be used for processing fault log data.

BRIEF DESCRIPTION OF THE INVENTION

Generally, the present invention in one exemplary embodiment fulfillsthe foregoing needs by providing a method for analyzing operationalparameter data from a malfunctioning locomotive or other largeland-based, self-powered transport equipment. The method allows forreceiving new operational parameter data comprising a plurality ofanomaly definitions from the malfunctioning equipment. The methodfurther allows for selecting a plurality of distinct anomaly definitionsfrom the new operational parameter data. Respective generating stepsallow for generating at least one distinct anomaly definition clusterfrom the plurality of distinct anomaly definitions and for generating aplurality of weighted repair and distinct anomaly definition clustercombinations. An identifying step allows for identifying at least onerepair for the at least one distinct anomaly definition cluster usingthe plurality of weighted repair and distinct anomaly definition clustercombinations.

The present invention further fulfills the foregoing needs by providingin another aspect thereof a system for analyzing operational parameterdata from a malfunctioning locomotive or other large land-based,self-powered transport equipment. The system includes a directed weightdata storage unit adapted to store a plurality of weighted repair anddistinct anomaly definition cluster combinations. A processor is adaptedto receive new operational parameter data comprising a plurality ofanomaly definitions from the malfunctioning equipment. Processor allowsfor selecting a plurality of distinct anomaly definitions from the newoperational parameter data. Processor further allows for generating atleast one distinct anomaly definition cluster from the selectedplurality of distinct anomaly definitions and for generating a pluralityof weighted repair and distinct anomaly definition cluster combinations.Processor 12 also allows for identifying at least one repair for the atleast one distinct anomaly definition cluster using the plurality ofpredetermined weighted repair and distinct anomaly definition clustercombinations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is one embodiment of a block diagram of a system of the presentinvention for automatically processing repair data and operationalparameter data from one or more machines and diagnosing a malfunctioningmachine;

FIG. 2 is an illustration of an exemplary data structure including datafields that may be used for specifying an anomaly definition andincluding exemplary new operational parameter data from a malfunctioningmachine;

FIG. 3 is a flowchart describing the steps for analyzing the newoperational parameter data from a malfunctioning machine and predictingone or more possible repair actions;

FIG. 4 is an illustration of distinct anomaly definitions identified inthe new operational parameter data, such as may be represented in FIG.2, and the number of occurrences thereof;

FIGS. 5A-5D are illustrations of distinct fault anomaly definitionclusters for the distinct faults identified in FIG. 4;

FIG. 6 is a flowchart describing the steps for generating a plurality ofpredetermined cases, and predetermined repair and anomaly definitioncluster combinations for each case;

FIG. 7 is a flowchart describing the steps for determining predeterminedweighted repair and anomaly definition cluster combinations;

FIG. 8 is a printout of weighted repair and anomaly definition clustercombinations provided by the system shown in FIG. 1 for operationalparameters that may be represented in FIG. 2, and a listing ofrecommended repairs;

FIG. 9 is a flowchart further describing the step of predicting repairsfrom the weighted repair and anomaly definition cluster combinationsshown in FIG. 8; and

FIG. 10 is one embodiment of a flowchart describing the steps forautomatically analyzing new operational parameter data from amalfunctioning machine and predicting one or more possible repairactions.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 diagrammatically illustrates one exemplary embodiment of a system10 of the present invention. In one aspect, system 10 provides automatedanalysis of operational parameter data, from a malfunctioning machinesuch as a locomotive, and prediction of one or more possible repairactions.

Although the present invention is described with reference to alocomotive, system 10 can be used in conjunction with any machine inwhich operation of the machine is monitored, such as a chemical, anelectronic, a mechanical, a microprocessor machine and any otherland-based, self-powered transport equipment.

Exemplary system 10 includes a processor 12 such as a computer (e.g.,UNIX workstation) having a hard drive, input devices such as a keyboard,a mouse, magnetic storage media (e.g., tape cartridges or disks),optical storage media (e.g., CD-ROMs), and output devices such as adisplay and a printer. Processor 12 is operably connected to a repairdata storage unit 20, an operational parameter data storage unit 22, acase data storage unit 24, and a directed weight data storage unit 26.

FIG. 2 shows an exemplary data structure 50 comprising a plurality ofdata fields, generally associated with anomaly definitions based onoperational parameter data. As shown in FIG. 2, a set of data fields 52may include general information regarding each anomaly definition, suchas anomaly definition identifier, objective, explanatory remarks,message to be automatically generated upon detection of a respectiveanomaly definition, personnel responsible for handling a respectiveanomaly definition, locomotive model and configuration, etc. As furthershown in FIG. 2, a set of data fields 54 may include observationsindicative of locomotive operating conditions that may be associatedwith an anomaly definition, including statistics data and trend datathat may be extracted from such observations. FIG. 2 further shows a setof data fields 56 that may include operational operational parameterdata that may be associated with a given anomaly definition. Forexample, if parameter 1 is outside a predefined range, and the standarddeviation of parameter 2 is beyond a predefined level, and parameter 3exhibits a trend that exceeds a predefined rate of change, and parameter4 is outside another predefined range under a given set of locomotiveoperating condition, then, assuming each of the above conditions is met,and further assuming that there is an anomaly definition specifying eachof such conditions, that would constitute detection of such anomalydefinition, that is, the occurrence of each of such events would triggerthat anomaly definition. It will be appreciated that the level ofinformation that can be obtained from anomaly definitions based onoperational parameter data comprising a selectable time window is morestatistically robust compared to fault log data that are based on theoccurrence of single instance events. The inventors of the presentinvention have advantageously recognized that diagnostics algorithmtechniques typically associated with the processing of fault log datamay now be extended to processing anomaly definitions based oncontinuous operational parameter data, as opposed to singular timeevents. As used herein operational parameter data refers to continuousor non-discrete data. That is, data that may be expressed in numericalranges such as engine speed, voltages, etc., or data that may bemonitored over a desired time window for trends, shifts, changes, etc.,as opposed to data indicative of discrete states. Of course, the termcontinuous data does not exclude digitally sampled data since such datamay be observed over a desired time window provided the sampling rate issufficiently fast relative to the time window to detect trends, shifts,changes, etc.

FIG. 3 is a flowchart which generally describes the steps for analyzingnew operational parameter data 200 (FIG. 1). As shown in FIG. 3 at 232,the new operational parameter data comprising a plurality of anomalydefinitions from a malfunctioning machine is received. At 233, aplurality of distinct anomaly definitions from the new operationalparameter data is identified, and at 234, the number of times eachdistinct anomaly definition occurred in the new operational parameterdata is determined. As used herein, the term “distinct anomalydefinition” is an anomaly definition or anomaly code which differs fromother anomaly definitions or anomaly codes so that, as described ingreater detail below, if the operational parameter data includes morethan one occurrence of the same anomaly definition or anomaly code, thensimilar anomaly definitions or anomaly codes are identified only once.As will become apparent from the discussion below, in one exemplaryembodiment, it is the selection or triggering of distinct anomalydefinitions which is important and not the order or sequence of theirarrangement.

FIG. 4 shows an exemplary plurality of distinct anomaly definitions andthe number of times in which each distinct anomaly definition occurredfor operational parameter 220 (FIG. 2). In this example, anomalydefinition code 7311 represents a phase module malfunction whichoccurred 24 times, anomaly definition code 728F indicates an inverterpropulsion malfunction which occurred twice, anomaly definition code76D5 indicates an anomaly definition which occurred once, and anomalydefinition code 720F indicates an inverter propulsion malfunction whichoccurred once.

With reference again to FIG. 3, a plurality of anomaly definitionclusters is generated for the distinct anomaly definitions at 236. FIGS.5A-5D illustrate the distinct anomaly definition clusters generated fromthe distinct anomaly definitions extracted from operational parameterdata 200. Four single anomaly definition clusters (e.g., anomalydefinition code 7311, anomaly definition code 728F, anomaly definitioncode 76D5, and anomaly definition code 720F) are illustrated in FIG. 5A.Six double anomaly definition clusters (e.g., anomaly definition codes76D5 and 7311, anomaly definition codes 76D5 and 728F, anomalydefinition codes 76D5 and 720F, anomaly definition codes 7311 and 728F,anomaly definition codes 7311 and 720F, and anomaly definition codes728F and 720F) are illustrated in FIG. 5B. Four triple anomalydefinition clusters (e.g., anomaly definition codes 76D5, 7311, and728F), anomaly definition codes 76D5, 7311, and 720F, anomaly definitioncodes 76D5, 728F, and 720F, and anomaly definition codes 7311, 728F, and720F) are illustrated in FIG. 5C, and one quadruple anomaly definitioncluster (e.g., 76D5, 7311, 728F, and 720F) is illustrated in FIG. 5D.

From the present description, it will be appreciated by those skilled inthe art that an anomaly definition log having a greater number ofdistinct anomaly definitions would result in a greater number ofdistinct anomaly definition clusters (e.g., ones, twos, threes, fours,fives, etc.).

At 238, at least one repair is predicted for the plurality of anomalydefinition clusters using a plurality of predetermined weighted repairand anomaly definition cluster combinations. The plurality ofpredetermined weighted repair and anomaly definition clustercombinations may be generated as follows.

With reference again to FIG. 1, processor 12 is desirably operable toprocess historical repair data contained in a repair data storage unit20 and historical operational parameter data contained in an operationalparameter data storage unit 22 regarding one or more locomotives.

For example, repair data storage unit 20 includes repair data or recordsregarding a plurality of related and unrelated repairs for one or morelocomotives. Operational parameter data storage unit 22 includesoperational parameter data or records regarding a plurality of anomalydefinitions occurring for one or more locomotives.

FIG. 6 is a flowchart of an exemplary process 50 of the presentinvention for selecting or extracting repair data from repair datastorage unit 20 and operational parameter data from the operationalparameter data storage unit 22 and generating a plurality of cases, andrepair and anomaly definition cluster combinations.

Exemplary process 50 comprises, at 52, selecting or extracting a repairfrom repair data storage unit 20 (FIG. 1). Given the identification of arepair, the present invention searches operational parameter datastorage unit 22 (FIG. 1) to select or extract anomaly definitionsoccurring over a predetermined period of time prior to the repair, at54. At 56, the number of times each distinct anomaly definition occurredduring the period of time is determined.

A repair and corresponding distinct anomaly definitions are summarizedand stored as a case, at 60. For each case, a plurality of repair andanomaly definition cluster combinations are generated at 62 (in asimilar manner as described for the new operational parameter data).

Process 50 is repeated by selecting another repair entry from repairdata to generate another case, and to generate a plurality of repair andanomaly definition cluster combinations. Case data storage unit 24desirably comprises a plurality of cases comprising related andunrelated repairs.

FIG. 7 is a flowchart of an exemplary process 100 of the presentinvention for generating weighted repair and anomaly definition clustercombinations based on the plurality of cases generated in process 50.Process 100 comprises, at 101, selecting a repair and anomaly definitioncluster combination, and determining, at 102, the number of times thecombination occurs for related repairs. The number of times thecombination occurs in the plurality of cases of related and unrelatedrepairs, e.g., all repairs for similar locomotives, is determined at104. A weight is determined at 108 for the repair and distinct anomalydefinition cluster combination by dividing the number of times thedistinct anomaly definition cluster occurs in related cases by thenumber of times the distinct anomaly definition cluster occurs in all,e.g., related and unrelated cases, and the weighted repair and distinctanomaly definition cluster combination is desirably stored in a directedweight data storage unit 26.

FIG. 8 illustrates an exemplary printout 250 of the results generated bysystem 10 (FIG. 1) based on operational parameter data 200 (FIG. 1), inwhich in a first portion 252, a plurality of corresponding repairs 253,assigned weights 254, and anomaly definition clusters 255 are presented.As shown in a second portion 260 of printout 250, five recommendationsfor likely repairs actions are presented for review by a field engineer.

FIG. 9 is a flowchart of an exemplary process 300 for determining andpresenting the top most likely repair candidates which may includerepairs derived from predetermined weighted repair and distinct anomalydefinition cluster combinations having the greatest assigned weightedvalues or repairs which are determined by adding together the assignedweighted values for anomaly definition clusters for related repairs.

As shown in FIG. 9, initially, a distinct anomaly definition clustergenerated from the new operational parameter data is selected at 302. At304, predetermined repair(s) and assigned weight(s) corresponding to thedistinct anomaly definition cluster are selected from directed weightstorage unit 26 (FIG. 1).

At 306, if the assigned weight for the predetermined weighted repair andanomaly definition cluster combination is determined by a plurality ofcases for related and unrelated repairs which number is less than apredetermined number, e.g., 5, the cluster is excluded and the nextdistinct anomaly definition cluster is selected at 302. This preventsweighted repair and anomaly definition cluster combinations which aredetermined from only a few cases from having the same effect in theprediction of repairs as weighted repair and anomaly definition clustercombinations determined from many cases.

If the number of cases is greater than the predetermined minimum numberof cases, at 308, a determination is made as to whether the assignedvalue is greater than a threshold value, e.g., 0.70 or 70%. If so, therepair is displayed at 310. If the anomaly definition cluster is not thelast anomaly definition cluster to be analyzed at 322, the next distinctanomaly definition cluster is selected at 302 and the process isrepeated.

If the assigned weight for the predetermined weighted repair and anomalydefinition cluster combination is less than the predetermined thresholdvalue, the assigned weights for related repairs are added together at320. Desirably, up to a maximum number of assigned weights, e.g., 5, areused and added together. After selecting and analyzing the distinctanomaly definition clusters generated from the new operational parameterdata, the repairs having the highest added assigned weights for anomalydefinition clusters for related repairs are displayed at 324.

With reference again to FIG. 8, repairs corresponding to the weightedrepair and anomaly definition cluster combinations in which the assignedweights are greater than the threshold value are presented first. Asshown in FIG. 8, repair codes 1766 and 1777 and distinct anomalydefinition cluster combinations 7311, 728F, and 720F, have an assignedweight of 85% and indicate a recommended replacement of the EFI.

As also shown in FIG. 8, repairs for various anomaly definition clustershaving the highest added or total weight are presented next. Forexample, repair code 1677 which corresponds to a traction problem has atotaled assigned weight of 1.031, repair code 1745 which corresponds toa locomotive software problem has a totaled assigned weight of 0.943,and repair code 2323 which corresponds to an overheated engine has atotaled assigned weight of 0.591.

Advantageously, the top five most likely repair actions are determinedand presented for review by a field engineer. For example, up to fiverepairs having the greatest assigned weights over the threshold valueare presented. When there is less than five repairs which satisfy thethreshold, the remainder of recommended repairs are presented based on atotal assigned weight.

Desirably the new operational parameter data is initially compared to aprior operational parameter data from the malfunctioning locomotive.This allows determination whether there is a change in the operationalparameter data over time. For example, if there is no change, e.g., nonew anomaly definitions, then it may not be necessary to process the newoperational parameter data further.

FIG. 10 illustrates a flowchart of an exemplary automated process 500for analyzing operational parameter data from a locomotive, e.g., newoperational parameter data which is generated every day, using system10. In particular, process 500 accommodates the situation where a priorrepair is undertaken or a prior repair is recommended within thepredetermined period of time over which the operational parameter datais analyzed. This avoids recommending the same repair which has beenpreviously recommended and/or repaired.

At 502, new operational parameter data is received which includesanomaly definitions occurring over a predetermined period of time, e.g.,14 days. The operational parameter data is analyzed, for example asdescribed above, generating distinct anomaly definition clusters andcomparing the generated anomaly definition clusters to predeterminedweighted repair and anomaly definition cluster combinations.

At 504, the analysis process may use a thresholding process describedabove to determine whether any repairs are recommended (e.g., having aweighted value over 70%). If no repairs are recommended, the process isended at 506. The process is desirably repeated again with a download ofnew operational parameter data the next day.

If a repair recommendation is made, existing closed (e.g., performed orcompleted repairs) or prior recommended repairs which have occurredwithin the predetermined period of time are determined at 508. Forexample, existing closed or prior recommended repairs may be stored andretrieved from repair data storage unit 20. If there are no existing orrecommended repairs than all the recommended repairs at 504 are listedin a repair list at 700.

If there are existing closed or prior recommended repairs, then at 600,any repairs not in the existing closed or prior recommended repairs arelisted in the repair list at 700.

For repairs which are in the existing closed or prior recommendedrepairs, at 602, the look-back period (e.g., the number of days overwhich the anomaly definitions are chosen) is revised. Using the modifiedlook-back or shortened period of time, the modified operationalparameter data is analyzed at 604, as described above, using distinctanomaly definition clusters, and comparing the generated anomalydefinition clusters to predetermined weighted repair and anomalydefinition cluster combinations.

At 606, the analysis process may use the thresholding process describedabove to determine whether any repairs are recommended (e.g., having aweighted value over 70%). If no repairs are recommended, the process isended at 608 until the process is stated again with a new operationalparameter data from the next day, or if a repair is recommended it isadded to the repair list at 700.

From the present description, it will be appreciated by those skilled inthe art that other processes and methods, e.g., different thresholdingvalues or operational parameter data analysis which does not usedistinct anomaly definition clusters, may be employed in predictingrepairs from the new operational parameter data according to process 500which takes into account prior performed repairs or prior recommendedrepairs.

Thus, the present invention provides in one aspect a method and systemfor processing a new operational parameter which is not restricted tosequential occurrences of anomaly definitions or error log entries. Inanother aspect, the calibration of the diagnostic significance ofanomaly definition clusters is based upon cases of related repairs andcases for all the repairs.

While the invention has been described with reference to preferredembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof without departing from the scope of the invention. Inaddition, many modifications may be made to adapt a particular situationor material to the teachings of the invention without departing from theessential scope thereof. Therefore, it is intended that the inventionnot be limited to the particular embodiments disclosed herein, but thatthe invention will include all embodiments falling within the scope ofthe appended claims.

1. A computer-implemented method for analyzing operational parameterdata from a locomotive to correct and/or prevent locomotivemalfunctions, the method comprising: receiving a set of operationalparameter data from the locomotive; identifying a plurality of distinctanomaly definitions in the set of operational parameter data; generatingat least one distinct anomaly cluster from the plurality of distinctanomaly definitions; and associating with said anomaly cluster at leastone repair for correcting and/or preventing occurrence of the locomotivemalfunction.
 2. The method of claim 1 further comprising generating aplurality of weighted repair and distinct anomaly cluster combinationsindicative of distinct locomotive malfunctions.
 3. The method of claim 2wherein the associating with said anomaly cluster of at least one repaircomprises using the plurality of weighted repair and distinct anomalycluster combinations to associate said at least one repair for the atleast one distinct anomaly cluster.
 4. The method of claim 1 wherein theat least one distinct anomaly cluster comprises at least one of a singledistinct anomaly and a plurality of distinct anomaly definitions.
 5. Themethod of claim 2 wherein each of the plurality of weighted repair anddistinct anomaly cluster combinations are generated from a plurality ofcases, each case comprising a repair and at least one distinct anomaly,and each of the plurality of weighted repair and distinct anomalycluster combinations being assigned a weight determined by dividing thenumber of times the combination occurs in cases comprising relatedrepairs by the total number of times the combination occurs in saidplurality of cases.
 6. The method of claim 5 further comprisingselecting at least one repair using the plurality of weighted repair andanomaly cluster combinations and adding assigned weights for distinctanomaly clusters for related repairs.
 7. The method of claim 2 whereinsaid generating a plurality of weighted repair and distinct anomalycluster combinations comprises using a plurality of repairs andoperational parameter data including a plurality of anomaly definitions.8. The method of claim 1 wherein the receiving operational parameterdata comprises receiving a new operational parameter data and comparingthe new operational parameter data to a prior operational parameterdata.
 9. A system for analyzing operational parameter data from amalfunctioning locomotive, comprising: a directed weight data storageunit adapted to store a plurality of weighted repair and distinctanomaly cluster combinations; a processor adapted to receive newoperational parameter data comprising a plurality of anomaly definitionsfrom the malfunctioning locomotive; a processor for selecting aplurality of distinct anomaly definitions from the new operationalparameter data; a processor for generating at least one distinct anomalydefinition cluster from the selected plurality of distinct anomalydefinitions; a processor for generating a plurality of weighted repairand distinct anomaly definition cluster combinations; and a processorfor identifying at least one repair for the at least one distinctanomaly definition cluster using the plurality of predetermined weightedrepair and distinct anomaly definition cluster combinations.
 10. Thesystem of claim 9 wherein a single processor unit constitutes saidprocessors.
 11. The system of claim 9 further comprising: a processorfor generating a plurality of cases from the repair data and theoperational parameter date, each case comprising a repair and aplurality of distinct anomaly definitions; a processor for generating,for each of the plurality of cases, at least one repair and distinctanomaly definition cluster combination; and a processor for assigning,to each of the repair and distinct anomaly definition clustercombinations, a weight, whereby weighted repair and distinct anomalydefinition cluster combinations facilitate identification of at leastone repair for the malfunctioning locomotive.
 12. The system of claim 11wherein the processor for generating the plurality of cases comprises aprocessor for selecting a repair from the repair data and selecting aplurality of distinct anomaly definitions from the operational parameterdata over a period of time prior to the repair.
 13. The system of claim11 wherein the processor for assigning weights comprises a processor fordetermining, for each repair and distinct anomaly definition dustercombination, a number of times the combination occurs in casescomprising related repairs, and a number of times the combination occursin the plurality of cases.
 14. The system of claim 13 wherein theprocessor for assigning a weight, for each repair and distinct anomalydefinition cluster combination, comprises a processor for dividing thenumber of times the combination occurs in cases comprising relatedrepairs by the number of times the combination occurs in the pluralityof cases.
 15. The system of claim 13 further comprising: a processor forgenerating a new case from repair data and operational parameter data,the case comprising a repair and a plurality of distinct anomalydefinitions; a processor for generating, for the new case, a pluralityof anomaly definition clusters for the plurality of distinct anomalydefinitions; and a processor for redetermining a weight for each of theplurality of repair and anomaly definition cluster combinations toinclude the new case.
 16. The system of claim 9 further comprising: arepair log data storage unit adapted to store a plurality of repairs;and an operational parameter data storage unit adapted to store aplurality of anomaly definitions.
 17. An article of manufacturecomprising: a computer program product comprising a computer-usablemedium having a computer-readable code therein for analyzing operationalparameter data from a locomotive to correct and/or prevent locomotivemalfunctions, the computer-readable code comprising: computer-readablecode for receiving a set of operational parameter data from thelocomotive; computer-readable code for identifying a plurality ofdistinct anomaly definitions in the set of operational parameter data;computer-readable code for generating at least one distinct anomalycluster from the plurality of distinct anomaly definitions; andcomputer-readable code for associating with said anomaly cluster atleast one repair for correcting and/or preventing occurrence of thelocomotive malfunction.
 18. The article of manufacture of claim 17further comprising computer-readable code for generating a plurality ofweighted repair and distinct anomaly cluster combinations indicative ofdistinct locomotive malfunctions.
 19. The article of manufacture ofclaim 18 wherein the computer-readable code for associating with saidanomaly cluster at least one repair comprises computer-readable code forusing the plurality of weighted repair and distinct anomaly dustercombinations to associate said at least one repair for the at least onedistinct anomaly cluster.
 20. The article of manufacture of claim 17wherein the at least one distinct anomaly cluster comprises at least oneof a single distinct anomaly and a plurality of distinct anomalydefinitions.
 21. The article of manufacture of claim 18 wherein each ofthe plurality of weighted repair and distinct anomaly clustercombinations are generated from a plurality of cases, each casecomprising a repair and at least one distinct anomaly, and each of theplurality of weighted repair and distinct anomaly cluster combinationsbeing assigned a weight determined by dividing the number of times thecombination occurs in cases comprising related repairs by the totalnumber of times the combination occurs in said plurality of cases. 22.The article of manufacture of claim 21 further comprisingcomputer-readable code for selecting at least one repair using theplurality of weighted repair and anomaly cluster combinations and addingassigned weights for distinct anomaly clusters for related repairs. 23.The article of manufacture of claim 18 wherein the computer-readablecode for generating a plurality of weighted repair and distinct anomalycluster combinations comprises using a plurality of repairs andoperational parameter data including a plurality of anomaly definitions.24. The article of manufacture of claim 17 wherein the computer-readablecode for receiving operational parameter data comprisescomputer-readable code for receiving a new operational parameter dataand comparing the new operational parameter data to a prior operationalparameter data.